Sitong Wu, Haoru Tan, Xichen Zhang, Bin Xia, Wenhu Zhang, Xiaojuan Qi, Bei Yu, Jiaya Jia. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Xichen Zhang, Ziyi He, Yinghao Zhu, Sitong Wu, Shaozuo Yu, Meng Chu, Wenhu Zhang, Haoru Tan, Jiaya Jia. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Trajectory imputation plays a vital role in intelligent transportation systems. However, real-world trajectory data often suffers from noise, missing segments, and limited labeled samples, which significantly hampers the performance of existing methods. Moreover, conventional approaches typically depend on large-scale labeled datasets or negative samples, limiting their scalability to large or sparsely annotated datasets.To address these challenges, we propose MATIBL, a Multi-level Augmented representation learning framework for Trajectory Imputation via Bootstrap Learning. MATIBL enhances trajectory representation learning through trajectory-specific data augmentations and task-aware objectives. Specifically, a dual-branch bootstrap pretraining strategy is employed to learn robust representations without requiring negative samples. A mixed augmentation scheme incorporating random dropout and Gaussian noise simulates real-world trajectory irregularities, while multi-faceted features—including spatial, temporal, and attribute information—enable comprehensive modeling. A BERT-based encoder supports position and order prediction tasks to capture fine-grained spatial-temporal dependencies.Extensive experiments on two large-scale real-world trajectory datasets (Chengdu and Porto) demonstrate that MATIBL consistently outperforms state-of-the-art methods. It maintains high accuracy under extreme missing rates (up to 90%) and exhibits strong generalization across diverse datasets, validating its effectiveness and practicality.

The U-Net models have become the predominant architecture within the domain of medical image segmentation. Recent advancements have showcased the potential of incorporating attention-based techniques into U-Net structures. Nevertheless, the inclusion of attention mechanisms often leads to a substantial increase in both computational demands and the number of parameters, with only a marginal improvement in the performance. This observation raises a critical evaluation of the efficiency associated with the integration of attention modules. In this paper, we propose a novel methodology termed Hierarchical Context Interaction (HCI), a parameter-efficient, attention-free enhancement that can be seamlessly incorporated into U-Net-based models. Experimental results demonstrate that our proposed HCI module attains state-of-the-art performance on two widely used benchmarks, i.e. Medical Segmentation Decathlon Datasets and Synapse Datasets, while concurrently sustaining a computationally efficient profile comparable to conventional U-Net configurations.

Gait recognition has a rapid development in recent years. However, current gait recognition focuses primarily on ideal laboratory scenes, leaving the gait in the wild unexplored. One of the main reasons is the difficulty of collecting in-the-wild gait datasets, which must ensure diversity of both intrinsic and extrinsic human gait factors. To remedy this problem, we propose to construct a large-scale gait dataset with the help of controllable computer simulation. In detail, to diversify the intrinsic factors of gait, we generate numerous characters with diverse attributes and associate them with various types of walking styles. To diversify the extrinsic factors of gait, we build a complicated scene with a dense camera layout. Then we design an automatic generation toolkit under Unity3D for simulating the walking scenarios and capturing the gait data. As a result, we obtain a dataset simulating towards the in-the-wild scenario, called VersatileGait, which has more than one million silhouette sequences of 10,000 subjects with diverse scenarios. VersatileGait possesses several nice properties, including huge dataset size, diverse pedestrian attributes, complicated camera layout, high-quality annotations, small domain gap with the real one, good scalability for new demands, and no privacy issues. By conducting a series of experiments, we first explore the effects of different factors on gait recognition. We further illustrate the effectiveness of using our dataset to pre-train models, which obtain considerable performance gain on CASIA-B, OU-MVLP, and CASIA-E. Besides, we show the great potential of the fine-grained labels other than the ID label in improving the efficiency and effectiveness of models. Our dataset and its corresponding generation toolkit are available at https://github.com/peterzpy/VersatileGait.

RGB-D salient object detection (SOD) is usually formulated as a problem of classification or regression over two modalities, i.e., RGB and depth. Hence, effective RGB-D feature modeling and multi-modal feature fusion both play a vital role in RGB-D SOD. In this paper, we propose a depth-sensitive RGB feature modeling scheme using the depth-wise geometric prior of salient objects. In principle, the feature modeling scheme is carried out in a depth-sensitive attention module, which leads to the RGB feature enhancement as well as the background distraction reduction by capturing the depth geometry prior. More-over, to perform effective multi-modal feature fusion, we further present an automatic architecture search approach for RGB-D SOD, which does well in finding out a feasible architecture from our specially designed multi-modal multi-scale search space. Extensive experiments on seven standard benchmarks demonstrate the effectiveness of the proposed approach against the state-of-the-art.